Sharpen your decision tree expertise during this one-day advanced course, geared towards analysts and modelers with prior knowledge of tree algorithms. Using case studies, seminar topics include:

• Hybridizing CART with logistic regression.
• Combining multiple trees via bagging, boosting, repeated sampling and varying priors.
• Understanding the strategy behind alternative splitting rules and their characteristic strengths, weaknesses and signatures.
• Learning how CART implements variable costs of misclassification; separating growing with costs from pruning with costs.
• Emerging topics in decision trees; highlights of recent developments.

MARS

What is MARS? Why does it work? How can it be used? How can it help you develop more accurate regression models for problems such as predicting credit card holder balances, insurance claim losses, customer catalog orders, and cell phone use?

RandomForests

RandomForests®, created by Leo Breiman and Adele Cutler, is based on learning ensembles of CART trees. By judiciously injecting randomness into the tree-building process and then combining hundreds of these trees, RF is able to deliver high performance predictive models and a variety of novel exploratory data analysis results. RF also incorporates new metric free CLUSTER analyses that automatically select the variables used to define each cluster, with potentially different variables defining each cluster.

Attendees will be introduced to the main concepts in boosting methods in data mining. They will also be presented with the core innovations behind TreeNet stochastic gradient boosting, including the concepts of slow learning, use of weak learners in every stage of model building, resampling from the training at every stage, and ignoring data considered too far from the decision boundary in classification problems.